I am doing a research on travel reviews and used word2vec to analyze the reviews. However, when I showed my output to my adviser, he said that I have a lot of words with negative vector values and that only words with positive values are considered logical.
What could these negative values mean? Is there a way to ensure that all vector values I will get in my analysis would be positive?
While some other word-modeling algorithms do in fact model words into spaces where dimensions are 0 or positive, and the individual positive dimensions might be clearly meaningful to humans, that is not the case with the original, canonical 'word2vec' algorithm.
The positive/negativeness of any word2vec word-vector – in a particular dimension, or in net magnitude – has no strong meaning. Meaningful words will be spread out in every direction from the origin point. Directions or neighborhoods in this space that loosely correlate to recognizable categories may appear anywhere, and skew with respect to any of the dimensional axes.
(Here's a related algorithm that does use non-negative constraints – https://www.cs.cmu.edu/~bmurphy/NNSE/. But most references to 'word2vec' mean the classic approach where dimensions usefully range over all reals.)
